Digital-image processing is a broad and a important field that provides foundation technologies for many types of diagnosis, monitoring, surveillance, data collection, and data analysis. Digital-image-processing methods are used in medical imaging, optical-character-recognition systems, processing and analysis of image data collected by surveillance, monitoring, and remote-sensing systems, production of informational and entertainment videos, and digital photography. A variety of sophisticated mathematical techniques have been developed to address many problem domains in digital-image processing, including reduction of noise in digital images, sharpening of imaged features in digital images, extraction of information from digital images, and many other such problem domains.
In many areas of digital-image processing and analysis, two or more different images are compared in order to extract differential information from the two or more images corresponding to various types of changes. As one example, frames of surveillance videos may be compared to one another by automated techniques in order to extract information related to temporal changes in the environment being monitored. Surveillance videos are often collected continuously from various types of environments, such as building entrances and the interiors of train stations and airports, and automated, analytical methodologies based on automated comparison of frames from such videos are applied to hours of days of recorded surveillance video in order to pinpoint particular anomalous events that occurred at particular points of time during surveillance. In other cases, frame-comparison-based methods are employed in real time to detect anomalous events and trigger alerts. As another example, a time sequence of digital medical images of a tumor or other pathology may be compared in order to detect changes in the tumor or other pathology over time. Often, digital images are compared by subtracting the intensity values in one image from the intensity values of another, in order to detect intensity differences between the two images. However, many intensity differences between images may arise from phenomena unrelated to the types of changes and events of interest. For example, the intensities in two different medical images may differ systematically due to differences in the imaging devices used to acquire the images, differences in image-device settings and parameters, random and systematic noise in the digital images, and many other types of intensity-altering phenomena. Normalization of images is therefore a major problem domain in image processing. Normalization seeks to reduce or eliminate systematic intensity variation between images, in preparation for image comparison, without losing or obscuring the meaningful, but often subtle, intensity differences that are sought to be extracted by digital-image-comparison techniques.